Real-Time Adaptive Music Generation via AI Segmentation

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Solution Overview

Problem

Content gatekeepers face challenges in providing adaptive music for digital media, such as video games and VR experiences, due to a lack of original music, complex copyright issues, and inefficient music acquisition methods, which disrupt user workflow and fail to meet the demand for custom, emotionally responsive soundtracks.

Innovation Solution

A real-time adaptive music generation system that decomposes music into machine-learnable building blocks, allowing for crowd-sourced data acquisition and real-time re-composition, enabling user-directed AI generation of musical scenarios that adapt to user interactions and emotional states, providing a seamless integration with digital platforms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional music libraries and copyright systems are used, then music availability is limited, but music variety and adaptability are reduced

Engineering Contradiction:

Improvemusic adaptabilityVSAvoidmusic variety

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system segments music into hierarchical components (musical units, phrases, sections, movements) that can be independently generated, manipulated, and recombined. This segmentation enables adaptive recombination of musical elements based on real-time context while maintaining musical coherence and structure.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements dynamic music generation where musical parameters (tempo, key, instrumentation, emotional tone) are continuously adjusted based on real-time user interactions, game state, and contextual parameters. This creates music that adapts dynamically rather than using static pre-composed tracks.

Inventive Principle:

Principle #15Dynamics

2Ease of manufacture

If professional composers are used to create custom music, then music quality is high, but production time and cost increase

Engineering Contradiction:

Improvemusic production efficiencyVSAvoidmusic creation time

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The system enables self-service music generation through AI algorithms that automatically compose, arrange, and produce music based on specified parameters and contextual input. Users can generate custom soundtracks without requiring professional composers, significantly reducing production time and cost while maintaining quality through sophisticated musical generation models.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary music generation and pre-composition of musical libraries in advance, creating reusable musical units and templates that can be quickly adapted and assembled. This preliminary preparation enables rapid music production when needed while maintaining high quality through pre-refined musical elements.

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If music libraries are searched manually, then existing music can be found, but user workflow is disrupted

Engineering Contradiction:

Improveworkflow continuityVSAvoidmusic acquisition time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system introduces an intermediary AI music generation service that sits between the user and traditional music libraries. This intermediary automatically generates or selects appropriate music based on contextual parameters, eliminating the need for users to manually search music libraries and maintain continuous workflow without interruption.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback loops where music generation is continuously refined based on user preferences, contextual parameters, and performance data. This feedback mechanism enables the system to learn and adapt to user needs over time, providing increasingly accurate and appropriate music selections without requiring manual intervention.

Inventive Principle:

Principle #23Feedback

4Productivity

If AI music generation is implemented, then music production speed increases, but music quality and emotional depth may decrease

Engineering Contradiction:

Improvemusic generation speedVSAvoidmusic quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system uses composite music generation approaches, combining multiple AI models specialized in different musical aspects (melody generation, harmony creation, rhythm generation, instrumentation). This composite approach leverages the strengths of each specialized model to produce high-quality, emotionally resonant music while maintaining rapid generation speeds through parallel processing.

Inventive Principle:

Principle #40Composite materials

Data Source

PatentUS20240038205A1Systems, apparatuses, and/or methods for real-time adaptive music generation

Publication Date: 2024.02.01 XSOLLA USA

AI summary

Disclosed herein are systems, apparatus, and/or methods for automatically generating in real-time an adaptive digital music stream that satisfies a particular scenario (either static or changing), as described by the dynamic input of an emotional vector (possibly with emotional direction/target), one or more musical styles and one or more musical themes, using an automated music composition, performance and audio production system based which utilizes machine learning and artificial intelligence techniques.